library(survival)
library(survminer)
library(dplyr)
setwd("M:/Elena - R/Multi-state cancer")
load("1 data prep/working.data.Rda")
# to all variable in the environment, group age, select complete cases, group obesity
e<-eapply(.GlobalEnv, function(x) {
x<-x%>%
mutate(age_gr.b= ifelse(age<=59, "Aged 18 to 59",
ifelse(age<=79, "Aged 60 to 79",
"Aged 80 or above" ))) %>%
mutate(age_gr.b = factor(age_gr.b, levels = c("Aged 18 to 59", "Aged 60 to 79","Aged 80 or above"))) %>%
mutate(age_2gr= ifelse(age<70, "Aged 18 to 69",
"Aged 70 or above")) %>%
mutate(age_2gr= factor(age_2gr, levels = c("Aged 18 to 69", "Aged 70 or above"))) %>%
mutate(smoke.all_time=factor(smoke.all_time))%>%
mutate(smoke.all_time=relevel(smoke.all_time, "Never smoker"))%>%
mutate(medea=relevel(medea, "U1"))%>%
droplevels()
output <- x
}
)
nms <- names(e)
for ( n in nms ){
assign(n, e[[n]])
}
Graphical check proportionality of variables for the cox model
Transition 1: From general population to diagnosed with COVID-19
Prior diagnosis of cancer
Cancer yes/no
survfit.t1.cancer <- survfit(Surv(time, status) ~ cancer,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t1.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t1.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t1.cancer <-arrange_ggsurvplots(survfit.t1.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t1.cancer
Years since cancer diagnosis
survfit.t1.c_status <- survfit(Surv(time, status) ~ c_status,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t1.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,100))+
theme(legend.title = element_blank())
survfit.t1.c_status<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurvplot.cloglog
survfit.t1.c_status <-arrange_ggsurvplots(survfit.t1.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t1.c_status
Hematological vs solid
survfit.t1.hem_solid <- survfit(Surv(time, status) ~ hem_solid,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1.hem_solid, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t1.hem_solid, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t1.hem_solid<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t1.hem_solid <-arrange_ggsurvplots(survfit.t1.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t1.hem_solid
Gender
survfit.t1.gender<-survfit(Surv(time, status) ~ gender,
data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t1.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t1.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t1.gender <-arrange_ggsurvplots(ggsurv.t1.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.gender
Age groups
survfit.t1.age_gr<-survfit(Surv(time, status) ~ age_gr.b,
data = r.healthy.diagnosis)
ggsurv.t1.age_gr<- list(
ggsurvplot(survfit.t1.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.age_gr <-arrange_ggsurvplots(ggsurv.t1.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.age_gr
survfit.t1.age_2gr<-survfit(Surv(time, status) ~ age_2gr,
data = r.healthy.diagnosis)
ggsurv.t1.age_2gr<- list(
ggsurvplot(survfit.t1.age_2gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.age_2gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.age_2gr <-arrange_ggsurvplots(ggsurv.t1.age_2gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.age_2gr
Smoke
survfit.t1.smoke<-survfit(Surv(time, status) ~ smoke.all_time,
data = r.healthy.diagnosis)
ggsurv.t1.smoke<- list(
ggsurvplot(survfit.t1.smoke, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.smoke <-arrange_ggsurvplots(ggsurv.t1.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.smoke
Medea
survfit.t1.medea<-survfit(Surv(time, status) ~ medea,
data = r.healthy.diagnosis)
ggsurv.t1.medea<- list(
ggsurvplot(survfit.t1.medea, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.medea <-arrange_ggsurvplots(ggsurv.t1.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.medea
Comorbidities
Autoinmune condition
survfit.t1.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.healthy.diagnosis)
ggsurv.t1.a_autoimmune_condition<- list(
ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t1.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_autoimmune_condition
Chronic Kidney Disease
survfit.t1.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.healthy.diagnosis)
ggsurv.t1.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t1.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_chronic_kidney_disease
COPD
survfit.t1.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.healthy.diagnosis)
ggsurv.t1.a_copd<- list(
ggsurvplot(survfit.t1.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_copd <-arrange_ggsurvplots(ggsurv.t1.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_copd
Dementia
survfit.t1.a_dementia<-survfit(Surv(time, status) ~ a_dementia ,
data = r.healthy.diagnosis)
ggsurv.t1.a_dementia<- list(
ggsurvplot(survfit.t1.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_dementia <-arrange_ggsurvplots(ggsurv.t1.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_dementia
Heart disease
survfit.t1.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.healthy.diagnosis)
ggsurv.t1.a_heart_disease<- list(
ggsurvplot(survfit.t1.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_heart_disease <-arrange_ggsurvplots(ggsurv.t1.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_heart_disease
Hyperlipidemia
survfit.t1.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.healthy.diagnosis)
ggsurv.t1.a_hyperlipidemia<- list(
ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t1.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_hyperlipidemia
Hypertension
survfit.t1.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.healthy.diagnosis)
ggsurv.t1.a_hypertension<- list(
ggsurvplot(survfit.t1.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_hypertension <-arrange_ggsurvplots(ggsurv.t1.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_hypertension
Obesity
survfit.t1.a_obesity<-survfit(Surv(time, status) ~ a_obesity,
data = r.healthy.diagnosis)
ggsurv.t1.a_obesity<- list(
ggsurvplot(survfit.t1.a_obesity, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_obesity <-arrange_ggsurvplots(ggsurv.t1.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_obesity
Type 2 Diabetes
survfit.t1.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.healthy.diagnosis)
ggsurv.t1.a_t2_diabetes<- list(
ggsurvplot(survfit.t1.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t1.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t1.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_t2_diabetes
Transition 2: From general population to hospitalised with COVID-19
Prior diagnosis of cancer
Cancer yes /no
survfit.t2.cancer <- survfit(Surv(time, status) ~ cancer,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t2.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t2.cancer <-arrange_ggsurvplots(survfit.t2.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t2.cancer
Years since cancer diagnosis
survfit.t2.c_status <- survfit(Surv(time, status) ~ c_status,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t2.c_status<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t2.c_status <-arrange_ggsurvplots(survfit.t2.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t2.c_status
Cancer type, hematological or solid vs cancer-free
survfit.t2.hem_solid <- survfit(Surv(time, status) ~ hem_solid,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.hem_solid, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.hem_solid, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t2.hem_solid<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t2.hem_solid <-arrange_ggsurvplots(survfit.t2.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t2.hem_solid
Gender
survfit.t2.gender<-survfit(Surv(time, status) ~ gender,
data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t2.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t2.gender <-arrange_ggsurvplots(ggsurv.t2.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.gender
Age groups
survfit.t2.age_gr<-survfit(Surv(time, status) ~ age_gr.b,
data = r.healthy.hospitalised)
ggsurv.t2.age_gr<- list(
ggsurvplot(survfit.t2.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.age_gr <-arrange_ggsurvplots(ggsurv.t2.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.age_gr
Smoke
survfit.t2.smoke<-survfit(Surv(time, status) ~ smoke.all_time,
data = r.healthy.hospitalised)
ggsurv.t2.smoke<- list(
ggsurvplot(survfit.t2.smoke, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.smoke <-arrange_ggsurvplots(ggsurv.t2.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.smoke
Medea
survfit.t2.medea<-survfit(Surv(time, status) ~ medea,
data = r.healthy.hospitalised)
ggsurv.t2.medea<- list(
ggsurvplot(survfit.t2.medea, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.medea <-arrange_ggsurvplots(ggsurv.t2.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.medea
Comorbidities
Autoinmune condition
survfit.t2.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.healthy.hospitalised)
ggsurv.t2.a_autoimmune_condition<- list(
ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t2.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_autoimmune_condition
Chronic Kidney Disease
survfit.t2.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.healthy.hospitalised)
ggsurv.t2.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t2.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_chronic_kidney_disease
COPD
survfit.t2.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.healthy.hospitalised)
ggsurv.t2.a_copd<- list(
ggsurvplot(survfit.t2.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_copd <-arrange_ggsurvplots(ggsurv.t2.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_copd
Dementia
survfit.t2.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.healthy.hospitalised)
ggsurv.t2.a_dementia<- list(
ggsurvplot(survfit.t2.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_dementia <-arrange_ggsurvplots(ggsurv.t2.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_dementia
Heart disease
survfit.t2.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.healthy.hospitalised)
ggsurv.t2.a_heart_disease<- list(
ggsurvplot(survfit.t2.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_heart_disease <-arrange_ggsurvplots(ggsurv.t2.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_heart_disease
Hyperlipidemia
survfit.t2.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.healthy.hospitalised)
ggsurv.t2.a_hyperlipidemia<- list(
ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t2.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_hyperlipidemia
Hypertension
survfit.t2.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.healthy.hospitalised)
ggsurv.t2.a_hypertension<- list(
ggsurvplot(survfit.t2.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_hypertension <-arrange_ggsurvplots(ggsurv.t2.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_hypertension
Obesity
survfit.t2.a_obesity<-survfit(Surv(time, status) ~ a_obesity,
data = r.healthy.hospitalised)
ggsurv.t2.a_obesity<- list(
ggsurvplot(survfit.t2.a_obesity, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_obesity <-arrange_ggsurvplots(ggsurv.t2.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_obesity
Type 2 DM
survfit.t2.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.healthy.hospitalised)
ggsurv.t2.a_t2_diabetes<- list(
ggsurvplot(survfit.t2.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t2.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t2.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_t2_diabetes
Transition 3: From general population to death
Prior diagnosis of cancer
Cancer yes/no
survfit.t3.cancer <- survfit(Surv(time, status) ~ cancer,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t3.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t3.cancer <-arrange_ggsurvplots(survfit.t3.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t3.cancer
Years since cancer diagnosis
#graphical check proportionality of variables for the cox model
survfit.t3.c_status<-survfit(Surv(time, status) ~ c_status,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t3.c_status<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t3.c_status <-arrange_ggsurvplots(survfit.t3.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t3.c_status
Cancer type, hematological or solid vs cancer-free
survfit.t3.hem_solid <- survfit(Surv(time, status) ~ hem_solid,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.hem_solid, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.hem_solid, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t3.hem_solid<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t3.hem_solid <-arrange_ggsurvplots(survfit.t3.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t3.hem_solid
Gender
survfit.t3.gender<-survfit(Surv(time, status) ~ gender,
data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t3.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t3.gender <-arrange_ggsurvplots(ggsurv.t3.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.gender
Age groups
survfit.t3.age_gr<-survfit(Surv(time, status) ~ age_gr.b,
data = r.healthy.death)
ggsurv.t3.age_gr<- list(
ggsurvplot(survfit.t3.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.age_gr <-arrange_ggsurvplots(ggsurv.t3.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.age_gr
Smoke
survfit.t3.smoke<-survfit(Surv(time, status) ~ smoke.all_time,
data = r.healthy.death)
ggsurv.t3.smoke<- list(
ggsurvplot(survfit.t3.smoke, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.smoke <-arrange_ggsurvplots(ggsurv.t3.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.smoke
Medea
survfit.t3.medea<-survfit(Surv(time, status) ~ medea,
data = r.healthy.death)
ggsurv.t3.medea<- list(
ggsurvplot(survfit.t3.medea, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.medea <-arrange_ggsurvplots(ggsurv.t3.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.medea
Comorbidities
Autoinmune condition
survfit.t3.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.healthy.death)
ggsurv.t3.a_autoimmune_condition<- list(
ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t3.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_autoimmune_condition
Chronic Kidney Disease
survfit.t3.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.healthy.death)
ggsurv.t3.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t3.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_chronic_kidney_disease
COPD
survfit.t3.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.healthy.death)
ggsurv.t3.a_copd<- list(
ggsurvplot(survfit.t3.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_copd <-arrange_ggsurvplots(ggsurv.t3.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_copd
Dementia
survfit.t3.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.healthy.death)
ggsurv.t3.a_dementia<- list(
ggsurvplot(survfit.t3.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_dementia <-arrange_ggsurvplots(ggsurv.t3.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_dementia
Heart disease
survfit.t3.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.healthy.death)
ggsurv.t3.a_heart_disease<- list(
ggsurvplot(survfit.t3.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_heart_disease <-arrange_ggsurvplots(ggsurv.t3.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_heart_disease
Hyperlipidemia
survfit.t3.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.healthy.death)
ggsurv.t3.a_hyperlipidemia<- list(
ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t3.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_hyperlipidemia
Hypertension
survfit.t3.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.healthy.death)
ggsurv.t3.a_hypertension<- list(
ggsurvplot(survfit.t3.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_hypertension <-arrange_ggsurvplots(ggsurv.t3.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_hypertension
Obesity
survfit.t3.a_obesity<-survfit(Surv(time, status) ~ a_obesity,
data = r.healthy.death)
ggsurv.t3.a_obesity<- list(
ggsurvplot(survfit.t3.a_obesity, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_obesity <-arrange_ggsurvplots(ggsurv.t3.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_obesity
Type 2 DM
survfit.t3.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.healthy.death)
ggsurv.t3.a_t2_diabetes<- list(
ggsurvplot(survfit.t3.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t3.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t3.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_t2_diabetes
Transition 4: From diagnosed with COVID-19 to hospitalised with COVID-19
Prior diagnosis of cancer
cancer yes/no
survfit.t4.cancer <- survfit(Surv(time, status) ~ cancer,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t4.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t4.cancer <-arrange_ggsurvplots(survfit.t4.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t4.cancer
Years since cancer diagnosis
#graphical check proportionality of variables for the cox model
survfit.t4.c_status<-survfit(Surv(time, status) ~ c_status,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t4.c_status<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t4.c_status <-arrange_ggsurvplots(survfit.t4.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t4.c_status
Cancer type, hematological or solid vs cancer-free
survfit.t4.hem_solid <- survfit(Surv(time, status) ~ hem_solid,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.hem_solid, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.hem_solid, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t4.hem_solid<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t4.hem_solid <-arrange_ggsurvplots(survfit.t4.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t4.hem_solid
Gender
survfit.t4.gender<-survfit(Surv(time, status) ~ gender,
data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t4.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t4.gender <-arrange_ggsurvplots(ggsurv.t4.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.gender
Age groups
survfit.t4.age_gr<-survfit(Surv(time, status) ~ age_gr.b,
data = r.diagnosis.hospitalised)
ggsurv.t4.age_gr<- list(
ggsurvplot(survfit.t4.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.age_gr <-arrange_ggsurvplots(ggsurv.t4.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.age_gr
Smoke
survfit.t4.smoke<-survfit(Surv(time, status) ~ smoke.all_time,
data = r.diagnosis.hospitalised)
ggsurv.t4.smoke<- list(
ggsurvplot(survfit.t4.smoke, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.smoke <-arrange_ggsurvplots(ggsurv.t4.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.smoke
Medea
survfit.t4.medea<-survfit(Surv(time, status) ~ medea,
data = r.diagnosis.hospitalised)
ggsurv.t4.medea<- list(
ggsurvplot(survfit.t4.medea, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.medea <-arrange_ggsurvplots(ggsurv.t4.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.medea
Comorbidities
Autoinmune condition
survfit.t4.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_autoimmune_condition<- list(
ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t4.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_autoimmune_condition
Chronic Kidney Disease
survfit.t4.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t4.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_chronic_kidney_disease
COPD
survfit.t4.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_copd<- list(
ggsurvplot(survfit.t4.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_copd <-arrange_ggsurvplots(ggsurv.t4.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_copd
Dementia
survfit.t4.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_dementia<- list(
ggsurvplot(survfit.t4.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_dementia <-arrange_ggsurvplots(ggsurv.t4.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_dementia
Heart disease
survfit.t4.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_heart_disease<- list(
ggsurvplot(survfit.t4.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_heart_disease <-arrange_ggsurvplots(ggsurv.t4.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_heart_disease
Hyperlipidemia
survfit.t4.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_hyperlipidemia<- list(
ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t4.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_hyperlipidemia
Hypertension
survfit.t4.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_hypertension<- list(
ggsurvplot(survfit.t4.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hypertension <-arrange_ggsurvplots(ggsurv.t4.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_hypertension
Obesity
survfit.t4.a_obesity<-survfit(Surv(time, status) ~ a_obesity,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_obesity<- list(
ggsurvplot(survfit.t4.a_obesity, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_obesity <-arrange_ggsurvplots(ggsurv.t4.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_obesity
Type 2 DM
survfit.t4.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.diagnosis.hospitalised)
ggsurv.t4.a_t2_diabetes<- list(
ggsurvplot(survfit.t4.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t4.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t4.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_t2_diabetes
Transition 5: From diagnosed with COVID-19 to death
Prior diagnosis of cancer
cancer yes/no
survfit.t5.cancer <- survfit(Surv(time, status) ~ cancer,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t5.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t5.cancer <-arrange_ggsurvplots(survfit.t5.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t5.cancer
Years since cancer diagnosis
#graphical check proportionality of variables for the cox model
survfit.t5.c_status<-survfit(Surv(time, status) ~ c_status,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t5.c_status<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t5.c_status <-arrange_ggsurvplots(survfit.t5.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t5.c_status
Cancer type, hematological or solid vs cancer-free
survfit.t5.hem_solid <- survfit(Surv(time, status) ~ hem_solid,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.hem_solid, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.hem_solid, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t5.hem_solid<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t5.hem_solid <-arrange_ggsurvplots(survfit.t5.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t5.hem_solid
Gender
survfit.t5.gender<-survfit(Surv(time, status) ~ gender,
data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t5.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t5.gender <-arrange_ggsurvplots(ggsurv.t5.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.gender
Age groups
survfit.t5.age_gr<-survfit(Surv(time, status) ~ age_gr.b,
data = r.diagnosis.death)
ggsurv.t5.age_gr<- list(
ggsurvplot(survfit.t5.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.age_gr <-arrange_ggsurvplots(ggsurv.t5.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.age_gr
Smoke
survfit.t5.smoke<-survfit(Surv(time, status) ~ smoke.all_time,
data = r.diagnosis.death)
ggsurv.t5.smoke<- list(
ggsurvplot(survfit.t5.smoke, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.smoke <-arrange_ggsurvplots(ggsurv.t5.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.smoke
Medea
survfit.t5.medea<-survfit(Surv(time, status) ~ medea,
data = r.diagnosis.death)
ggsurv.t5.medea<- list(
ggsurvplot(survfit.t5.medea, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.medea <-arrange_ggsurvplots(ggsurv.t5.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.medea
Comorbidities
Autoinmune condition
survfit.t5.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.diagnosis.death)
ggsurv.t5.a_autoimmune_condition<- list(
ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t5.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_autoimmune_condition
Chronic Kidney Disease
survfit.t5.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.diagnosis.death)
ggsurv.t5.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t5.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t5.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_chronic_kidney_disease
COPD
survfit.t5.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.diagnosis.death)
ggsurv.t5.a_copd<- list(
ggsurvplot(survfit.t5.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_copd <-arrange_ggsurvplots(ggsurv.t5.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_copd
Dementia
survfit.t5.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.diagnosis.death)
ggsurv.t5.a_dementia<- list(
ggsurvplot(survfit.t5.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_dementia <-arrange_ggsurvplots(ggsurv.t5.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_dementia
Heart disease
survfit.t5.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.diagnosis.death)
ggsurv.t5.a_heart_disease<- list(
ggsurvplot(survfit.t5.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_heart_disease <-arrange_ggsurvplots(ggsurv.t5.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_heart_disease
Hyperlipidemia
survfit.t5.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.diagnosis.death)
ggsurv.t5.a_hyperlipidemia<- list(
ggsurvplot(survfit.t5.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t5.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_hyperlipidemia
Hypertension
survfit.t5.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.diagnosis.death)
ggsurv.t5.a_hypertension<- list(
ggsurvplot(survfit.t5.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_hypertension <-arrange_ggsurvplots(ggsurv.t5.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_hypertension
Obesity
survfit.t5.a_obesity<-survfit(Surv(time, status) ~ a_obesity,
data = r.diagnosis.death)
ggsurv.t5.a_obesity<- list(
ggsurvplot(survfit.t5.a_obesity, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_obesity <-arrange_ggsurvplots(ggsurv.t5.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_obesity
Type 2 DM
survfit.t5.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.diagnosis.death)
ggsurv.t5.a_t2_diabetes<- list(
ggsurvplot(survfit.t5.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t5.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t5.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_t2_diabetes
Transition 6: From hospitalised with COVID-19 to death
Prior diagnosis of cancer
cancer yes/no
survfit.t6.cancer <- survfit(Surv(time, status) ~ cancer,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t6.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t6.cancer <-arrange_ggsurvplots(survfit.t6.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t6.cancer
Years since cancer diagnosis
survfit.t6.c_status<-survfit(Surv(time, status) ~ c_status,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t6.c_status<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t6.c_status <-arrange_ggsurvplots(survfit.t6.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t6.c_status
Cancer type, hematological or solid vs cancer-free
survfit.t6.hem_solid <- survfit(Surv(time, status) ~ hem_solid,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.hem_solid, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.hem_solid, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
survfit.t6.hem_solid<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
survfit.t6.hem_solid <-arrange_ggsurvplots(survfit.t6.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t6.hem_solid
Gender
survfit.t6.gender<-survfit(Surv(time, status) ~ gender,
data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
theme(legend.position = "none")
ggsurv.t6.gender<- list(
ggsurvplot.event,
ggsurvplot.cloglog)
ggsurv.t6.gender <-arrange_ggsurvplots(ggsurv.t6.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.gender
Age groups
survfit.t6.age_gr<-survfit(Surv(time, status) ~ age_gr.b,
data = r.hospitalised.death)
ggsurv.t6.age_gr<- list(
ggsurvplot(survfit.t6.age_gr, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.age_gr <-arrange_ggsurvplots(ggsurv.t6.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.age_gr
Smoke
survfit.t6.smoke<-survfit(Surv(time, status) ~ smoke.all_time,
data = r.hospitalised.death)
ggsurv.t6.smoke<- list(
ggsurvplot(survfit.t6.smoke, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.smoke <-arrange_ggsurvplots(ggsurv.t6.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.smoke
Medea
survfit.t6.medea<-survfit(Surv(time, status) ~ medea,
data = r.hospitalised.death)
ggsurv.t6.medea<- list(
ggsurvplot(survfit.t6.medea, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.medea <-arrange_ggsurvplots(ggsurv.t6.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.medea
Comorbidities
Autoinmune condition
survfit.t6.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition,
data = r.hospitalised.death)
ggsurv.t6.a_autoimmune_condition<- list(
ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t6.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_autoimmune_condition
Chronic Kidney Disease
survfit.t6.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease,
data = r.hospitalised.death)
ggsurv.t6.a_chronic_kidney_disease<- list(
ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t6.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_chronic_kidney_disease
COPD
survfit.t6.a_copd<-survfit(Surv(time, status) ~ a_copd,
data = r.hospitalised.death)
ggsurv.t6.a_copd<- list(
ggsurvplot(survfit.t6.a_copd, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_copd <-arrange_ggsurvplots(ggsurv.t6.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_copd
Dementia
survfit.t6.a_dementia<-survfit(Surv(time, status) ~ a_dementia,
data = r.hospitalised.death)
ggsurv.t6.a_dementia<- list(
ggsurvplot(survfit.t6.a_dementia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_dementia <-arrange_ggsurvplots(ggsurv.t6.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_dementia
Heart disease
survfit.t6.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease,
data = r.hospitalised.death)
ggsurv.t6.a_heart_disease<- list(
ggsurvplot(survfit.t6.a_heart_disease, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_heart_disease <-arrange_ggsurvplots(ggsurv.t6.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_heart_disease
Hyperlipidemia
survfit.t6.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia,
data = r.hospitalised.death)
ggsurv.t6.a_hyperlipidemia<- list(
ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t6.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_hyperlipidemia
Hypertension
survfit.t6.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension,
data = r.hospitalised.death)
ggsurv.t6.a_hypertension<- list(
ggsurvplot(survfit.t6.a_hypertension, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_hypertension <-arrange_ggsurvplots(ggsurv.t6.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_hypertension
Obesity
survfit.t6.a_obesity<-survfit(Surv(time, status) ~ a_obesity,
data = r.hospitalised.death)
ggsurv.t6.a_obesity<- list(
ggsurvplot(survfit.t6.a_obesity, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_obesity <-arrange_ggsurvplots(ggsurv.t6.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_obesity
Type 2 DM
survfit.t6.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes,
data = r.hospitalised.death)
ggsurv.t6.a_t2_diabetes<- list(
ggsurvplot(survfit.t6.a_t2_diabetes, fun = "event", conf.int =TRUE),
ggsurvplot(survfit.t6.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t6.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_t2_diabetes
